abel_keya_week14_IP_Part 3_Association Rules
#https://rpubs.com/Jmdhcc/mba2 #https://rstudio-pubs-static.s3.amazonaws.com/192221_2390ff48c0a64fcb9b0f6ad6b1b2b7f2.html
#http://www.socr.umich.edu/people/dinov/courses/DSPA_notes/11_Apriory_AssocRuleLearning.html
library(plyr)
library(arulesViz)
library(RColorBrewer)
library(ggplot2)
# Load libraries
library(tidyverse) # data manipulation
library(arules) # mining association rules and frequent itemsets
#library(arulesViz) # visualization techniques for association rules
library(knitr) # dynamic report generation
library(gridExtra) # provides a number of user-level functions to work with "grid" graphics
library(lubridate) # work with dates and times# Loading our dataset
Supermarket_Sales <- read.transactions("Supermarket_Sales_Dataset II.csv", sep = ",")removing duplicated items in transactionssummary(Supermarket_Sales)transactions as itemMatrix in sparse format with
7501 rows (elements/itemsets/transactions) and
119 columns (items) and a density of 0.03288973
most frequent items:
mineral water eggs spaghetti french fries chocolate
1788 1348 1306 1282 1229
(Other)
22405
element (itemset/transaction) length distribution:
sizes
1 2 3 4 5 6 7 8 9 10 11 12 13 14
1754 1358 1044 816 667 493 391 324 259 139 102 67 40 22
15 16 18 19 20
17 4 1 2 1
Min. 1st Qu. Median Mean 3rd Qu. Max.
1.000 2.000 3.000 3.914 5.000 20.000
includes extended item information - examples:# look at the first five transactions
inspect(Supermarket_Sales[1:5]) items
[1] {almonds,
antioxydant juice,
avocado,
cottage cheese,
energy drink,
frozen smoothie,
green grapes,
green tea,
honey,
low fat yogurt,
mineral water,
olive oil,
salad,
salmon,
shrimp,
spinach,
tomato juice,
vegetables mix,
whole weat flour,
yams}
[2] {burgers,
eggs,
meatballs}
[3] {chutney}
[4] {avocado,
turkey}
[5] {energy bar,
green tea,
milk,
mineral water,
whole wheat rice} # examine the frequency of items
itemFrequency(Supermarket_Sales[, 1:3]) almonds antioxydant juice asparagus
0.020397280 0.008932142 0.004799360 # plot the frequency of items
itemFrequencyPlot(Supermarket_Sales, support = 0.1)
itemFrequencyPlot(Supermarket_Sales, topN = 20)
# a visualization of the sparse matrix for the first five transactions
image(Supermarket_Sales[1:5])
# visualization of a random sample of 100 transactions
image(sample(Supermarket_Sales, 100))
#Training a model on the data
library(arules)
# default settings result in zero rules learned
apriori(Supermarket_Sales)Apriori
Parameter specification:
Algorithmic control:
Absolute minimum support count: 750
set item appearances ...[0 item(s)] done [0.01s].
set transactions ...[119 item(s), 7501 transaction(s)] done [0.04s].
sorting and recoding items ... [7 item(s)] done [0.01s].
creating transaction tree ... done [0.00s].
checking subsets of size 1 2 done [0.01s].
writing ... [0 rule(s)] done [0.00s].
creating S4 object ... done [0.02s].
set of 0 rules # set better support and confidence levels to learn more rules
Supermarket_Sales_rules <- apriori(Supermarket_Sales, parameter = list(support =0.006, confidence = 0.25, minlen = 2))Apriori
Parameter specification:
Algorithmic control:
Absolute minimum support count: 45
set item appearances ...[0 item(s)] done [0.03s].
set transactions ...[119 item(s), 7501 transaction(s)] done [0.04s].
sorting and recoding items ... [97 item(s)] done [0.03s].
creating transaction tree ... done [0.00s].
checking subsets of size 1 2 3 4 done [0.06s].
writing ... [272 rule(s)] done [0.02s].
creating S4 object ... done [0.00s].Supermarket_Sales_rulesset of 272 rules #Evaluating model performance
# summary of grocery association rules
summary(Supermarket_Sales_rules)set of 272 rules
rule length distribution (lhs + rhs):sizes
2 3
90 182
Min. 1st Qu. Median Mean 3rd Qu. Max.
2.000 2.000 3.000 2.669 3.000 3.000
summary of quality measures:
support confidence coverage lift
Min. :0.006133 Min. :0.2500 Min. :0.01133 Min. :1.054
1st Qu.:0.006932 1st Qu.:0.2854 1st Qu.:0.02040 1st Qu.:1.561
Median :0.008532 Median :0.3241 Median :0.02573 Median :1.783
Mean :0.011492 Mean :0.3454 Mean :0.03502 Mean :1.872
3rd Qu.:0.012132 3rd Qu.:0.3960 3rd Qu.:0.03919 3rd Qu.:2.067
Max. :0.059725 Max. :0.5765 Max. :0.23837 Max. :4.122
count
Min. : 46.0
1st Qu.: 52.0
Median : 64.0
Mean : 86.2
3rd Qu.: 91.0
Max. :448.0
mining info:# inspect the first three rules
inspect(Supermarket_Sales_rules[1:3])# Improving model performance
# sorting grocery rules by lift
inspect(sort(Supermarket_Sales_rules, by = "lift")[1:5])# finding subsets of rules containing any herb & pepper
herb_pepper_rules <- subset(Supermarket_Sales_rules, items %in% "herb & pepper")
inspect(herb_pepper_rules)# writing the rules to a CSV file
write(Supermarket_Sales_rules, file = "Supermarket_Sales_rules.csv",
sep = ",", quote = TRUE, row.names = FALSE)
# converting the rule set to a data frame
Supermarket_Sales_rules_df <- as(Supermarket_Sales_rules, "data.frame")
# The rules identified by the algorithms
str(Supermarket_Sales_rules_df)'data.frame': 272 obs. of 6 variables:
$ rules : chr "{tomato sauce} => {spaghetti}" "{light cream} => {mineral water}" "{protein bar} => {mineral water}" "{meatballs} => {spaghetti}" ...
$ support : num 0.00627 0.00733 0.00773 0.0064 0.00773 ...
$ confidence: num 0.443 0.47 0.417 0.306 0.369 ...
$ coverage : num 0.0141 0.0156 0.0185 0.0209 0.0209 ...
$ lift : num 2.55 1.97 1.75 1.76 1.55 ...
$ count : int 47 55 58 48 58 51 58 51 49 57 ...Supermarket_Sales_rules_df$rules [1] "{tomato sauce} => {spaghetti}"
[2] "{light cream} => {mineral water}"
[3] "{protein bar} => {mineral water}"
[4] "{meatballs} => {spaghetti}"
[5] "{meatballs} => {mineral water}"
[6] "{energy bar} => {mineral water}"
[7] "{rice} => {mineral water}"
[8] "{parmesan cheese} => {spaghetti}"
[9] "{almonds} => {eggs}"
[10] "{almonds} => {mineral water}"
[11] "{light mayo} => {french fries}"
[12] "{vegetables mix} => {mineral water}"
[13] "{french wine} => {spaghetti}"
[14] "{french wine} => {mineral water}"
[15] "{oil} => {chocolate}"
[16] "{oil} => {mineral water}"
[17] "{fresh tuna} => {spaghetti}"
[18] "{fresh tuna} => {mineral water}"
[19] "{pepper} => {spaghetti}"
[20] "{pepper} => {mineral water}"
[21] "{ham} => {spaghetti}"
[22] "{ham} => {mineral water}"
[23] "{cereals} => {green tea}"
[24] "{cereals} => {milk}"
[25] "{cereals} => {spaghetti}"
[26] "{cereals} => {mineral water}"
[27] "{red wine} => {eggs}"
[28] "{red wine} => {spaghetti}"
[29] "{red wine} => {mineral water}"
[30] "{butter} => {chocolate}"
[31] "{whole wheat pasta} => {olive oil}"
[32] "{whole wheat pasta} => {milk}"
[33] "{whole wheat pasta} => {spaghetti}"
[34] "{whole wheat pasta} => {mineral water}"
[35] "{cottage cheese} => {mineral water}"
[36] "{hot dogs} => {spaghetti}"
[37] "{hot dogs} => {mineral water}"
[38] "{tomato juice} => {mineral water}"
[39] "{brownies} => {mineral water}"
[40] "{avocado} => {mineral water}"
[41] "{fresh bread} => {mineral water}"
[42] "{salmon} => {chocolate}"
[43] "{salmon} => {spaghetti}"
[44] "{salmon} => {mineral water}"
[45] "{honey} => {spaghetti}"
[46] "{honey} => {mineral water}"
[47] "{herb & pepper} => {ground beef}"
[48] "{herb & pepper} => {eggs}"
[49] "{herb & pepper} => {spaghetti}"
[50] "{herb & pepper} => {mineral water}"
[51] "{grated cheese} => {spaghetti}"
[52] "{grated cheese} => {mineral water}"
[53] "{soup} => {milk}"
[54] "{soup} => {spaghetti}"
[55] "{soup} => {mineral water}"
[56] "{cooking oil} => {chocolate}"
[57] "{cooking oil} => {spaghetti}"
[58] "{cooking oil} => {mineral water}"
[59] "{whole wheat rice} => {mineral water}"
[60] "{turkey} => {eggs}"
[61] "{turkey} => {spaghetti}"
[62] "{turkey} => {mineral water}"
[63] "{chicken} => {spaghetti}"
[64] "{chicken} => {mineral water}"
[65] "{frozen smoothie} => {mineral water}"
[66] "{low fat yogurt} => {mineral water}"
[67] "{tomatoes} => {spaghetti}"
[68] "{tomatoes} => {mineral water}"
[69] "{olive oil} => {milk}"
[70] "{olive oil} => {spaghetti}"
[71] "{olive oil} => {mineral water}"
[72] "{shrimp} => {chocolate}"
[73] "{shrimp} => {spaghetti}"
[74] "{shrimp} => {mineral water}"
[75] "{cake} => {mineral water}"
[76] "{burgers} => {french fries}"
[77] "{burgers} => {eggs}"
[78] "{burgers} => {mineral water}"
[79] "{pancakes} => {spaghetti}"
[80] "{pancakes} => {mineral water}"
[81] "{frozen vegetables} => {spaghetti}"
[82] "{frozen vegetables} => {mineral water}"
[83] "{ground beef} => {spaghetti}"
[84] "{ground beef} => {mineral water}"
[85] "{milk} => {spaghetti}"
[86] "{milk} => {mineral water}"
[87] "{chocolate} => {mineral water}"
[88] "{eggs} => {mineral water}"
[89] "{spaghetti} => {mineral water}"
[90] "{mineral water} => {spaghetti}"
[91] "{salmon,spaghetti} => {mineral water}"
[92] "{mineral water,salmon} => {spaghetti}"
[93] "{ground beef,herb & pepper} => {spaghetti}"
[94] "{herb & pepper,spaghetti} => {ground beef}"
[95] "{ground beef,herb & pepper} => {mineral water}"
[96] "{herb & pepper,mineral water} => {ground beef}"
[97] "{grated cheese,spaghetti} => {mineral water}"
[98] "{grated cheese,mineral water} => {spaghetti}"
[99] "{milk,soup} => {mineral water}"
[100] "{mineral water,soup} => {milk}"
[101] "{soup,spaghetti} => {mineral water}"
[102] "{mineral water,soup} => {spaghetti}"
[103] "{cooking oil,eggs} => {mineral water}"
[104] "{cooking oil,mineral water} => {eggs}"
[105] "{cooking oil,spaghetti} => {mineral water}"
[106] "{cooking oil,mineral water} => {spaghetti}"
[107] "{spaghetti,whole wheat rice} => {mineral water}"
[108] "{mineral water,whole wheat rice} => {spaghetti}"
[109] "{milk,turkey} => {mineral water}"
[110] "{mineral water,turkey} => {milk}"
[111] "{spaghetti,turkey} => {mineral water}"
[112] "{mineral water,turkey} => {spaghetti}"
[113] "{chicken,milk} => {mineral water}"
[114] "{chicken,mineral water} => {milk}"
[115] "{chicken,chocolate} => {mineral water}"
[116] "{chicken,mineral water} => {chocolate}"
[117] "{chicken,spaghetti} => {mineral water}"
[118] "{chicken,mineral water} => {spaghetti}"
[119] "{frozen smoothie,milk} => {mineral water}"
[120] "{frozen smoothie,mineral water} => {milk}"
[121] "{frozen smoothie,spaghetti} => {mineral water}"
[122] "{frozen smoothie,mineral water} => {spaghetti}"
[123] "{eggs,low fat yogurt} => {mineral water}"
[124] "{low fat yogurt,mineral water} => {eggs}"
[125] "{frozen vegetables,tomatoes} => {spaghetti}"
[126] "{spaghetti,tomatoes} => {frozen vegetables}"
[127] "{milk,tomatoes} => {mineral water}"
[128] "{mineral water,tomatoes} => {milk}"
[129] "{spaghetti,tomatoes} => {mineral water}"
[130] "{mineral water,tomatoes} => {spaghetti}"
[131] "{frozen vegetables,olive oil} => {mineral water}"
[132] "{ground beef,olive oil} => {spaghetti}"
[133] "{olive oil,spaghetti} => {ground beef}"
[134] "{ground beef,olive oil} => {mineral water}"
[135] "{milk,olive oil} => {spaghetti}"
[136] "{olive oil,spaghetti} => {milk}"
[137] "{milk,olive oil} => {mineral water}"
[138] "{mineral water,olive oil} => {milk}"
[139] "{chocolate,olive oil} => {spaghetti}"
[140] "{olive oil,spaghetti} => {chocolate}"
[141] "{chocolate,olive oil} => {mineral water}"
[142] "{mineral water,olive oil} => {chocolate}"
[143] "{olive oil,spaghetti} => {mineral water}"
[144] "{mineral water,olive oil} => {spaghetti}"
[145] "{frozen vegetables,shrimp} => {mineral water}"
[146] "{mineral water,shrimp} => {frozen vegetables}"
[147] "{milk,shrimp} => {mineral water}"
[148] "{mineral water,shrimp} => {milk}"
[149] "{chocolate,shrimp} => {spaghetti}"
[150] "{shrimp,spaghetti} => {chocolate}"
[151] "{chocolate,shrimp} => {mineral water}"
[152] "{mineral water,shrimp} => {chocolate}"
[153] "{shrimp,spaghetti} => {mineral water}"
[154] "{mineral water,shrimp} => {spaghetti}"
[155] "{cake,milk} => {mineral water}"
[156] "{cake,french fries} => {mineral water}"
[157] "{cake,eggs} => {mineral water}"
[158] "{cake,mineral water} => {eggs}"
[159] "{cake,spaghetti} => {mineral water}"
[160] "{cake,mineral water} => {spaghetti}"
[161] "{burgers,milk} => {spaghetti}"
[162] "{burgers,spaghetti} => {milk}"
[163] "{burgers,milk} => {mineral water}"
[164] "{burgers,mineral water} => {milk}"
[165] "{burgers,french fries} => {eggs}"
[166] "{burgers,eggs} => {french fries}"
[167] "{burgers,chocolate} => {spaghetti}"
[168] "{burgers,spaghetti} => {chocolate}"
[169] "{burgers,eggs} => {spaghetti}"
[170] "{burgers,spaghetti} => {eggs}"
[171] "{burgers,eggs} => {mineral water}"
[172] "{burgers,mineral water} => {eggs}"
[173] "{burgers,spaghetti} => {mineral water}"
[174] "{burgers,mineral water} => {spaghetti}"
[175] "{frozen vegetables,pancakes} => {mineral water}"
[176] "{ground beef,pancakes} => {spaghetti}"
[177] "{pancakes,spaghetti} => {ground beef}"
[178] "{ground beef,pancakes} => {mineral water}"
[179] "{milk,pancakes} => {mineral water}"
[180] "{french fries,pancakes} => {mineral water}"
[181] "{chocolate,pancakes} => {spaghetti}"
[182] "{pancakes,spaghetti} => {chocolate}"
[183] "{chocolate,pancakes} => {mineral water}"
[184] "{mineral water,pancakes} => {chocolate}"
[185] "{eggs,pancakes} => {spaghetti}"
[186] "{pancakes,spaghetti} => {eggs}"
[187] "{eggs,pancakes} => {mineral water}"
[188] "{pancakes,spaghetti} => {mineral water}"
[189] "{mineral water,pancakes} => {spaghetti}"
[190] "{frozen vegetables,ground beef} => {spaghetti}"
[191] "{frozen vegetables,spaghetti} => {ground beef}"
[192] "{frozen vegetables,ground beef} => {mineral water}"
[193] "{frozen vegetables,mineral water} => {ground beef}"
[194] "{frozen vegetables,milk} => {chocolate}"
[195] "{chocolate,frozen vegetables} => {milk}"
[196] "{frozen vegetables,milk} => {eggs}"
[197] "{eggs,frozen vegetables} => {milk}"
[198] "{frozen vegetables,milk} => {spaghetti}"
[199] "{frozen vegetables,spaghetti} => {milk}"
[200] "{frozen vegetables,milk} => {mineral water}"
[201] "{frozen vegetables,mineral water} => {milk}"
[202] "{french fries,frozen vegetables} => {mineral water}"
[203] "{chocolate,frozen vegetables} => {spaghetti}"
[204] "{frozen vegetables,spaghetti} => {chocolate}"
[205] "{chocolate,frozen vegetables} => {mineral water}"
[206] "{frozen vegetables,mineral water} => {chocolate}"
[207] "{eggs,frozen vegetables} => {mineral water}"
[208] "{frozen vegetables,mineral water} => {eggs}"
[209] "{frozen vegetables,spaghetti} => {mineral water}"
[210] "{frozen vegetables,mineral water} => {spaghetti}"
[211] "{green tea,ground beef} => {spaghetti}"
[212] "{ground beef,milk} => {chocolate}"
[213] "{chocolate,ground beef} => {milk}"
[214] "{ground beef,milk} => {spaghetti}"
[215] "{milk,spaghetti} => {ground beef}"
[216] "{ground beef,milk} => {mineral water}"
[217] "{ground beef,mineral water} => {milk}"
[218] "{chocolate,ground beef} => {eggs}"
[219] "{eggs,ground beef} => {chocolate}"
[220] "{chocolate,ground beef} => {spaghetti}"
[221] "{chocolate,ground beef} => {mineral water}"
[222] "{ground beef,mineral water} => {chocolate}"
[223] "{eggs,ground beef} => {spaghetti}"
[224] "{eggs,ground beef} => {mineral water}"
[225] "{ground beef,spaghetti} => {mineral water}"
[226] "{ground beef,mineral water} => {spaghetti}"
[227] "{mineral water,spaghetti} => {ground beef}"
[228] "{eggs,green tea} => {french fries}"
[229] "{chocolate,green tea} => {spaghetti}"
[230] "{green tea,spaghetti} => {chocolate}"
[231] "{chocolate,green tea} => {mineral water}"
[232] "{eggs,green tea} => {mineral water}"
[233] "{green tea,spaghetti} => {mineral water}"
[234] "{green tea,mineral water} => {spaghetti}"
[235] "{french fries,milk} => {chocolate}"
[236] "{french fries,milk} => {eggs}"
[237] "{french fries,milk} => {spaghetti}"
[238] "{french fries,milk} => {mineral water}"
[239] "{chocolate,milk} => {eggs}"
[240] "{eggs,milk} => {chocolate}"
[241] "{chocolate,eggs} => {milk}"
[242] "{chocolate,milk} => {spaghetti}"
[243] "{milk,spaghetti} => {chocolate}"
[244] "{chocolate,spaghetti} => {milk}"
[245] "{chocolate,milk} => {mineral water}"
[246] "{milk,mineral water} => {chocolate}"
[247] "{chocolate,mineral water} => {milk}"
[248] "{eggs,milk} => {spaghetti}"
[249] "{milk,spaghetti} => {eggs}"
[250] "{eggs,milk} => {mineral water}"
[251] "{milk,mineral water} => {eggs}"
[252] "{eggs,mineral water} => {milk}"
[253] "{milk,spaghetti} => {mineral water}"
[254] "{milk,mineral water} => {spaghetti}"
[255] "{mineral water,spaghetti} => {milk}"
[256] "{chocolate,eggs} => {french fries}"
[257] "{french fries,spaghetti} => {chocolate}"
[258] "{french fries,mineral water} => {chocolate}"
[259] "{french fries,spaghetti} => {eggs}"
[260] "{french fries,spaghetti} => {mineral water}"
[261] "{french fries,mineral water} => {spaghetti}"
[262] "{chocolate,eggs} => {spaghetti}"
[263] "{chocolate,spaghetti} => {eggs}"
[264] "{eggs,spaghetti} => {chocolate}"
[265] "{chocolate,eggs} => {mineral water}"
[266] "{chocolate,mineral water} => {eggs}"
[267] "{eggs,mineral water} => {chocolate}"
[268] "{chocolate,spaghetti} => {mineral water}"
[269] "{chocolate,mineral water} => {spaghetti}"
[270] "{mineral water,spaghetti} => {chocolate}"
[271] "{eggs,spaghetti} => {mineral water}"
[272] "{eggs,mineral water} => {spaghetti}" # Verifying the object's class
#
class(Supermarket_Sales)[1] "transactions"
attr(,"package")
[1] "arules"# Absolute Item Frequency Plot
itemFrequencyPlot(Supermarket_Sales, topN=15, type="absolute", col="wheat2",xlab="Item name",
ylab="Frequency (absolute)", main="Absolute Item Frequency Plot")
# Generating a summary of the transaction dataset
# information such as the most purchased items,
# distribution of the item sets (no. of items purchased in each transaction),
summary(Supermarket_Sales)transactions as itemMatrix in sparse format with
7501 rows (elements/itemsets/transactions) and
119 columns (items) and a density of 0.03288973
most frequent items:
mineral water eggs spaghetti french fries chocolate
1788 1348 1306 1282 1229
(Other)
22405
element (itemset/transaction) length distribution:
sizes
1 2 3 4 5 6 7 8 9 10 11 12 13 14
1754 1358 1044 816 667 493 391 324 259 139 102 67 40 22
15 16 18 19 20
17 4 1 2 1
Min. 1st Qu. Median Mean 3rd Qu. Max.
1.000 2.000 3.000 3.914 5.000 20.000
includes extended item information - examples:# Create an item frequency plot for the top 20 items
if (!require("RColorBrewer")) {
# install color package of R
install.packages("RColorBrewer")
#include library RColorBrewer
library(RColorBrewer)
}itemFrequency(Supermarket_Sales) almonds antioxydant juice asparagus
0.0203972804 0.0089321424 0.0047993601
avocado babies food bacon
0.0333288895 0.0045327290 0.0086655113
barbecue sauce black tea blueberries
0.0107985602 0.0142647647 0.0091987735
body spray bramble brownies
0.0114651380 0.0018664178 0.0337288362
bug spray burger sauce burgers
0.0086655113 0.0058658845 0.0871883749
butter cake candy bars
0.0301293161 0.0810558592 0.0097320357
carrots cauliflower cereals
0.0153312892 0.0047993601 0.0257299027
champagne chicken chili
0.0467937608 0.0599920011 0.0061325157
chocolate chocolate bread chutney
0.1638448207 0.0042660979 0.0041327823
cider clothes accessories cookies
0.0105319291 0.0083988801 0.0803892814
cooking oil corn cottage cheese
0.0510598587 0.0047993601 0.0318624183
cream dessert wine eggplant
0.0009332089 0.0043994134 0.0131982402
eggs energy bar energy drink
0.1797093721 0.0270630583 0.0266631116
escalope extra dark chocolate flax seed
0.0793227570 0.0119984002 0.0090654579
french fries french wine fresh bread
0.1709105453 0.0225303293 0.0430609252
fresh tuna fromage blanc frozen smoothie
0.0222636982 0.0135981869 0.0633248900
frozen vegetables gluten free bar grated cheese
0.0953206239 0.0069324090 0.0523930143
green beans green grapes green tea
0.0086655113 0.0090654579 0.1321157179
ground beef gums ham
0.0982535662 0.0134648714 0.0265297960
hand protein bar herb & pepper honey
0.0051993068 0.0494600720 0.0474603386
hot dogs ketchup light cream
0.0323956806 0.0043994134 0.0155979203
light mayo low fat yogurt magazines
0.0271963738 0.0765231302 0.0109318757
mashed potato mayonnaise meatballs
0.0041327823 0.0061325157 0.0209305426
melons milk mineral water
0.0119984002 0.1295827223 0.2383682176
mint mint green tea muffins
0.0174643381 0.0055992534 0.0241301160
mushroom cream sauce napkins nonfat milk
0.0190641248 0.0006665778 0.0103986135
oatmeal oil olive oil
0.0043994134 0.0230635915 0.0658578856
pancakes parmesan cheese pasta
0.0950539928 0.0198640181 0.0157312358
pepper pet food pickles
0.0265297960 0.0065324623 0.0059992001
protein bar red wine rice
0.0185308626 0.0281295827 0.0187974937
salad salmon salt
0.0049326756 0.0425276630 0.0091987735
sandwich shallot shampoo
0.0045327290 0.0077323024 0.0049326756
shrimp soda soup
0.0714571390 0.0062658312 0.0505265965
spaghetti sparkling water spinach
0.1741101187 0.0062658312 0.0070657246
strawberries strong cheese tea
0.0213304893 0.0077323024 0.0038661512
tomato juice tomato sauce tomatoes
0.0303959472 0.0141314491 0.0683908812
toothpaste turkey vegetables mix
0.0081322490 0.0625249967 0.0257299027
water spray white wine whole weat flour
0.0003999467 0.0165311292 0.0093320891
whole wheat pasta whole wheat rice yams
0.0294627383 0.0585255299 0.0114651380
yogurt cake zucchini
0.0273296894 0.0094654046 itemFrequencyPlot(Supermarket_Sales,topN=20,type="absolute",col=brewer.pal(8,'Pastel2'), main="Absolute Item Frequency Plot")
itemFrequencyPlot(Supermarket_Sales,topN=20,type="relative",col=brewer.pal(8,'Pastel2'),main="Relative Item Frequency Plot")
association.rules <- apriori(Supermarket_Sales, parameter = list(supp=0.001, conf=0.8,maxlen=10))Apriori
Parameter specification:
Algorithmic control:
Absolute minimum support count: 7
set item appearances ...[0 item(s)] done [0.02s].
set transactions ...[119 item(s), 7501 transaction(s)] done [0.04s].
sorting and recoding items ... [116 item(s)] done [0.01s].
creating transaction tree ... done [0.00s].
checking subsets of size 1 2 3 4 5 6 done [0.09s].
writing ... [74 rule(s)] done [0.15s].
creating S4 object ... done [0.07s].summary(association.rules)set of 74 rules
rule length distribution (lhs + rhs):sizes
3 4 5 6
15 42 16 1
Min. 1st Qu. Median Mean 3rd Qu. Max.
3.000 4.000 4.000 4.041 4.000 6.000
summary of quality measures:
support confidence coverage lift
Min. :0.001067 Min. :0.8000 Min. :0.001067 Min. : 3.356
1st Qu.:0.001067 1st Qu.:0.8000 1st Qu.:0.001333 1st Qu.: 3.432
Median :0.001133 Median :0.8333 Median :0.001333 Median : 3.795
Mean :0.001256 Mean :0.8504 Mean :0.001479 Mean : 4.823
3rd Qu.:0.001333 3rd Qu.:0.8889 3rd Qu.:0.001600 3rd Qu.: 4.877
Max. :0.002533 Max. :1.0000 Max. :0.002666 Max. :12.722
count
Min. : 8.000
1st Qu.: 8.000
Median : 8.500
Mean : 9.419
3rd Qu.:10.000
Max. :19.000
mining info:inspect(association.rules[1:10])shorter.association.rules<- apriori(Supermarket_Sales, parameter = list(supp=0.001, conf=0.8, maxlen=3))Apriori
Parameter specification:
Algorithmic control:
Absolute minimum support count: 7
set item appearances ...[0 item(s)] done [0.01s].
set transactions ...[119 item(s), 7501 transaction(s)] done [0.04s].
sorting and recoding items ... [116 item(s)] done [0.01s].
creating transaction tree ... done [0.00s].
checking subsets of size 1 2 3Mining stopped (maxlen reached). Only patterns up to a length of 3 returned! done [0.06s].
writing ... [15 rule(s)] done [0.03s].
creating S4 object ... done [0.03s].subset.rules<- which(colSums(is.subset(association.rules, association.rules))>1)
length(subset.rules)[1] 12subset.association.rules. <- association.rules[-subset.rules]bacon.association.rules <- apriori(Supermarket_Sales, parameter = list(supp=0.001, conf=0.8), appearance = list(default="lhs", rhs= "bacon"))Apriori
Parameter specification:
Algorithmic control:
Absolute minimum support count: 7
set item appearances ...[1 item(s)] done [0.05s].
set transactions ...[119 item(s), 7501 transaction(s)] done [0.05s].
sorting and recoding items ... [116 item(s)] done [0.02s].
creating transaction tree ... done [0.00s].
checking subsets of size 1 2 3 4 5 6 done [0.06s].
writing ... [0 rule(s)] done [0.06s].
creating S4 object ... done [0.05s].inspect(head(bacon.association.rules))subRules <- association.rules[quality(association.rules)$confidence>0.4]
plot(subRules)
plot(subRules, method = "two-key plot")
plotly_arules(subRules)'plotly_arules' is deprecated.
Use 'plot' instead.
See help("Deprecated")To reduce overplotting, jitter is added! Use jitter = 0 to prevent jitter.top10subRules <- head(subRules, n=10, by = "confidence")
plot(top10subRules, method = "graph", engine= "htmlwidget")subRules2<-head(subRules, n=20, by="lift")
plot(subRules2, method = "paracoord")
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